Read in Data
# Get the Data
food_consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-18/food_consumption.csv')
## Rows: 1430 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, food_category
## dbl (2): consumption, co2_emmission
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Or read in with tidytuesdayR package (https://github.com/thebioengineer/tidytuesdayR)
# PLEASE NOTE TO USE 2020 DATA YOU NEED TO USE tidytuesdayR version ? from GitHub
# Either ISO-8601 date or year/week works!
# Install via devtools::install_github("thebioengineer/tidytuesdayR")
tuesdata <- tidytuesdayR::tt_load('2020-02-18')
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## --- Compiling #TidyTuesday Information for 2020-02-18 ----
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## --- There is 1 file available ---
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## --- Starting Download ---
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## Downloading file 1 of 1: `food_consumption.csv`
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## --- Download complete ---
tuesdata <- tidytuesdayR::tt_load(2020, week = 8)
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## --- Compiling #TidyTuesday Information for 2020-02-18 ----
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## --- There is 1 file available ---
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## --- Starting Download ---
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## Downloading file 1 of 1: `food_consumption.csv`
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## --- Download complete ---
food_consumption <- tuesdata$food_consumption
food_consumption
## # A tibble: 1,430 × 4
## country food_category consumption co2_emmission
## <chr> <chr> <dbl> <dbl>
## 1 Argentina Pork 10.5 37.2
## 2 Argentina Poultry 38.7 41.5
## 3 Argentina Beef 55.5 1712
## 4 Argentina Lamb & Goat 1.56 54.6
## 5 Argentina Fish 4.36 6.96
## 6 Argentina Eggs 11.4 10.5
## 7 Argentina Milk - inc. cheese 195. 278.
## 8 Argentina Wheat and Wheat Products 103. 19.7
## 9 Argentina Rice 8.77 11.2
## 10 Argentina Soybeans 0 0
## # … with 1,420 more rows
Data Wrangling
food_consumption_total <- food_consumption %>%
group_by(food_category) %>%
summarize(total_consumption = sum(consumption),
total_emission = sum(co2_emmission)) %>%
mutate(food_type = case_when(food_category == "Pork" ~ "Animal Product",
food_category == "Poultry" ~ "Animal Product",
food_category == "Beef" ~ "Animal Product",
food_category == "Fish" ~ "Animal Product",
food_category == "Eggs" ~ "Animal Product",
food_category == "Milk - inc. cheese" ~ "Animal Product",
TRUE ~ "Non-Animal Product"))
food_consumption_total
## # A tibble: 11 × 4
## food_category total_consumption total_emission food_type
## <chr> <dbl> <dbl> <chr>
## 1 Beef 1576. 48633. Animal Product
## 2 Eggs 1061. 975. Animal Product
## 3 Fish 2247. 3588. Animal Product
## 4 Lamb & Goat 338. 11837. Non-Animal Product
## 5 Milk - inc. cheese 16351. 23290 Animal Product
## 6 Nuts inc. Peanut Butter 538. 952. Non-Animal Product
## 7 Pork 2096. 7419. Animal Product
## 8 Poultry 2758. 2963. Animal Product
## 9 Rice 3819. 4887. Non-Animal Product
## 10 Soybeans 112. 50.4 Non-Animal Product
## 11 Wheat and Wheat Products 9301. 1774. Non-Animal Product
Visualization
plot_ly(data = food_consumption_total,
type = "treemap",
labels = ~ food_category,
values = ~ total_consumption)